Why focus on spatial visualisations?

• Spatial case is a good example to work through because the aesthetics we have to express estimates are limited
• Maps take up most of the usual aesthetics by being a representation of space
  • position, size, shape, etc all have an implicit meaning in the mapping context
  • colour/fill is usually the only aesthetic we have left
  • can also get creative and do glyph maps (we will ignore this variation here)
• Once we have filled in a map, colour/fill is often the only aesthetic that has

Citizen Scientist Data

• There have been reports of a strange spatial pattern in the temperatures of Iowa
• We get some citizen scientists to measure data at their home and report back
• To maintain anonymity, we are only provided with the county of each scientist

990 citizen scientists participated

We could just plot the data…

• We often get spatial data in terms of longitude and latitude which we can plot directly

• This approach is easy but lacks the contextual information that gives our plots meaning.

Spatial features objects

• SF objects are differentiated from a tibble because of additional metata in the Coordinate reference system (CRS). Specifically:
  • Assumptions about the shape of the planet (geodetic datum)
  • Distortions we will/won’t accept when drawing the map (map projection)

Can you see the spatial trend?

Estimate the county mean

• Visualising an estimate, such as a mean, can make trends easier to see
  • Should use the sampling distribution, but often we do not bother…

# Calculate County Mean
toy_temp |> 
  group_by(county_name) |>
  summarise(temp_mean = mean(recorded_temp),
            temp_se = sd(recorded_temp)/sqrt(n()),
            n = n()) 

Can you see the trend now?

Common Map Visualisations

Spot the difference

Exercise

Make the high and low variance choropleth maps yourself, and see why they come out looking identical

Solution 1: add an axis for uncertainty

• Pro
  • Included uncertainty and increased transparency
• Cons
  • High uncertainty signal still very visible
  • 2D palette is harder to read
    • Colour is not a simple 3D space
    • Using saturation hurts accessibility

Solution 2: blend the colours together

• Pros
  • Included uncertainty and increased transparency
  • Removed false signals
• Cons
  • Still have 2D Colour palette
  • Standard error at which to blend colours is made up
    • Impossible to align with hypothesis testing

Solution 3: simulate a sample

• Pros
  • Included uncertainty
  • High uncertainty interferes with reading of plot (?)
  • 1D colour palette

Popular R packages

• ggdibbler
  • Data: distribution from distributional
  • Maps: pixel map, other non-spatial maps
• Vizumap
  • Data: Depends on the plot. Can take a distribution as a q function (pixel), or an estimate and standard error as two variables (bivar/VSUP and glyph)
  • Maps: Biar/VSUP, pixel, glyph
• biscale
  • Data: Estimate and standard error as two variables
  • Maps: Biar/VSUP map

Alternative software for incorperating uncertainty

• Existing tidy data structures are not great for uncertain data
• e.g. Vizumap
  • Makes Bivariate maps and Pixel (sample) maps
  • Package is designed specifically for uncertainty
• Issues
  • ggplot2 flexibility is lost
    • e.g. you can only use one of three specific palettes
  • Very computationally expensive
    • A simple map can take over a minute to run
  • Need to make every component separately then combine

ggplot2 uses the grammar of graphics

It is designed to take in data

Not theoretical distributions

This is what ggdibbler is for

Basic ggdibbler Example

library(ggdibbler)
toy_temp_dist |> 
  ggplot() + 
  geom_sf_sample(aes(geometry = county_geometry,
                     fill=temp_dist))

Can utilise ggplot2 flexibility

ggplot(toy_temp_dist) +
  geom_sf_sample(aes(geometry=county_geometry, fill=temp_dist),  linewidth=0, n=7) +
  geom_sf(aes(geometry = county_geometry), fill=NA, linewidth=0.5, colour="white") +
  theme_minimal() +
  scale_fill_distiller(palette = "YlOrRd", direction= 1) +
  xlab("Longitude") +
  ylab("Latitude") +
  labs(fill = "Temperature") +
  ggtitle("A super cool and customised plot")

Remember, the plot is random

ggplot(toy_temp_dist) +
  geom_sf_sample(aes(geometry=county_geometry, fill=temp_dist),  linewidth=0, n=7) +
  geom_sf(aes(geometry = county_geometry), fill=NA, linewidth=0.5, colour="white") +
  theme_minimal() +
  scale_fill_distiller(palette = "YlOrRd", direction= 1) +
  xlab("Longitude") +
  ylab("Latitude") +
  labs(fill = "Temperature") +
  ggtitle("A super cool and customised plot")

Exercise

Here is the code that was used to make the cartogram from earlier in the session. Can you make a ggdibbler verion of this plot?

# Transform to a the crs needed to do the cartogram transformation
toy_merc <- st_transform(toy_temp_mean, 3857)
# cartogram transformation
toy_cartogram <- cartogram_cont(toy_merc, weight = "n", itermax = 5)
# Transform back to original crs 
toy_cartogram <- st_transform(toy_cartogram, st_crs(toy_temp_mean))

# Plot cartogram using ggplot2
ggplot(toy_cartogram) +
  geom_sf(aes(fill = temp_mean), linewidth = 0, alpha = 0.9) +
  theme_minimal() +
  scale_fill_distiller(palette = "YlOrRd", direction= 1) +
  xlab("Longitude") +
  ylab("Latitude") +
  labs(fill = "Temperature") +
  theme(aspect.ratio=0.7)

Solution

# only change to data is distribution
toy_cartogram |>
  mutate(temp_dist = distributional::dist_normal(temp_mean, temp_se^2)) |>
  ggplot() +
  geom_sf_sample(aes(geometry=county_geometry, 
                     fill=temp_dist), linewidth=0) +
   geom_sf(aes(geometry=county_geometry), fill=NA, colour="white") +
  theme_minimal() +
  scale_fill_distiller(palette = "YlOrRd", direction= 1) +
  xlab("Longitude") +
  ylab("Latitude") +
  labs(fill = "Temperature") +
  theme(aspect.ratio=0.7)